import matplotlib.pyplot as pl
%matplot inline
def learning_curves(X_train, y_train, X_test, y_test):
""" Calculates the performance of several models with varying sizes of training data.
    The learning and testing error rates for each model are then plotted. """

print ("Creating learning curve graphs for max_depths of 1, 3, 6, and 10. . .")

# Create the figure window
fig = pl.figure(figsize=(10,8))

# We will vary the training set size so that we have 50 different sizes
sizes = np.rint(np.linspace(1, len(X_train), 50)).astype(int)
train_err = np.zeros(len(sizes))
test_err = np.zeros(len(sizes))

# Create four different models based on max_depth
for k, depth in enumerate([1,3,6,10]):

    for i, s in enumerate(sizes):

        # Setup a decision tree regressor so that it learns a tree with max_depth = depth
        regressor = DecisionTreeRegressor(max_depth = depth)

        # Fit the learner to the training data
        regressor.fit(X_train[:s], y_train[:s])

        # Find the performance on the training set
        train_err[i] = performance_metric(y_train[:s], regressor.predict(X_train[:s]))

        # Find the performance on the testing set
        test_err[i] = performance_metric(y_test, regressor.predict(X_test))

    # Subplot the learning curve graph
    ax = fig.add_subplot(2, 2, k+1)

    ax.plot(sizes, test_err, lw = 2, label = 'Testing Error')
    ax.plot(sizes, train_err, lw = 2, label = 'Training Error')
    ax.legend()
    ax.set_title('max_depth = %s'%(depth))
    ax.set_xlabel('Number of Data Points in Training Set')
    ax.set_ylabel('Total Error')
    ax.set_xlim([0, len(X_train)])

# Visual aesthetics
fig.suptitle('Decision Tree Regressor Learning Performances', fontsize=18, y=1.03)
fig.tight_layout()
fig.show()